Should a data scientist know big data hadoop?

1
Should a data scientist know big data hadoop?
2

• A data scientist could do well to understand the
  way the MapReduce programming paradigm functions.
  This permits you to do exactly the exact same job
  in less time, or even considerably more work at
  precisely the exact same moment.

3
The part of interest in Hadoop will be MapReduce

• If you are visiting the sphere of big data and
  Hadoop today you are lucky because there
  currently exist several off the shelf
  implementations of these calculations that you
  want as a information scientist who are composed
  for Hadoop along with other programs such as
  Apache Spark. It's possible to use one of many
  data science libraries on the market written for
  these programs.
• Though you ought to be able to receive some
  excellent answers from those tools without
  digging too much to the implementations, then you
  might still wish to find out a little about how
  they operate since from the sphere of large data,
  execution is more significant than everbefore.
  And not only the platforms however also the
  algorithms beneath techniques such as SVMs, and
  k-means.
• Also Read Is it tough to learn big data Hadoop?

4
When it doesn't make a difference

• In the event that you were choosing between two
  different algorithms assembled into something
  such as R in your notebook, state, naive bayes
  versus k-nearest neighbors, then it may not make
  much difference concerning time to train and
  confirm your model along with the opportunity to
  use it to new information. However, in the sphere
  of big information a little difference in
  calculations' runtime can mean the difference
  between obtaining and response in a couple of
  minutes versus hours or maybe days.
• Another cause of paying attention to the
  advancements in large numbers is not only is it
  information science essential to develop a page
  of amounts that may not make sense when only
  considering them into a clear model from which
  you are able to acquire helpful predictions
  today it's the situation that you may have the
  type of information which, in the event that you
  just had a page value, could be understandable by
  simply looking at it, but you have a lot of it
  and also you need to turn into information
  science practices to summarize and make sense of
  everything.
• Examine the case of a journalist if seeking to
  make sense of a trove of records which have only
  been published. Each is readable but there might
  be tens of thousands of these. Data science
  methods in the context of large data are the sole
  tractable means to find a feeling for the
  information inside such scenarios.
• Learn Big Data and Hadoop by taking Big Data
  Training in Delhi from Madrid Software Training
  Solutions.